4 Ways That Data Informs Decisions in Game Development

Why Data-Driven Decisions in Game Development Matter

Instead of relying on assumptions, you can leverage your game’s data to make data-driven decisions. This allows you to:

• Better understand player behavior and preferences

• Refine gameplay mechanics

• Optimize monetization strategies

• Increase player retention.

But how exactly can you make these benefits a reality for your game? We’ve got you covered.

4 Ways Game Developers Can Make Data-Driven Decisions

We’ve zoomed in on four key areas of game development where data can play a vital part in decision-making: Monetization, gameplay, retention, and churn prediction.

1. Refining Monetization Strategies

In 2024, mobile game IAP revenue rose to $81 billion, with 4% YoY. And developers must lean on data to maximize their own.

By analyzing data such as churn points and purchasing behavior, developers can optimize monetization models such as:

• Pricing tiers

• In-game purchases (which 79% of games implement)

• Ad placements

• Subscriptions.

For example, Candy Crush Saga developer King has:

• Used predictive analytics to project play spending behavior.

• Increased targeted offers by identifying the likeliest players to buy extra lives and boosters.

• Identified players likelier to make in-game purchases.

• Optimized various marketing tactics to boost player engagement and revenue.

• Tracked perceived value of items and their impact to balance in-game economies.

• Analyzed player churn to build more effective retention strategies.

2. Improving Gameplay with Iterative Testing

It's standard practice for developers to test their game on players repeatedly. By analyzing large data sets from each test, they can optimize various game aspects, such as gameplay.

This allows them to identify patterns in player behavior and preferences. In turn, this helps them:

• Balance difficulty levels

• Improve player engagement and satisfaction

• Fine-tune game mechanics more accurately.

A/B testing is a similar method for making data-driven decisions. The steps are as follows:

1. Create multiple versions of a mechanic or feature. 

2. Monitor their impact on player experience. 

3. Compare each outcome to work out which one is most effective. 

4. Adjust the game accordingly.

This feedback loop is an essential way to continuously refine your game.

3. Improving Retention with Player Segmentation

One method for improving retention rates in mobile games is player segmentation. This strategy allows developers to personalize their game to different player cohorts. It's tailored to their specific behavior and preferences.

One game publisher that excels in this is Ubisoft. The makers of Assassin’s Creed and Far Cry use their game data to segment players and develop retention strategies. 

Ubisoft uses clustering algorithms to segment players based on:

• Spending behavior

• Playtime

• In-game achievements.

With this tactic, they can pinpoint players at risk of churning. This way, they can offer exclusive in-game rewards and personalized content to build loyalty.

4. Predicting and Preventing Player Churn 

Generally, D1 retention rates of 50% are the typical standard in mobile games. So, being able to predict churn effectively is more essential than ever. 

Many developers utilize churn prediction models to project which future players might churn. But it’s also to act quickly so that they keep returning to the game.

Three ways to build a churn prediction model include:

1. Identifying early signs of churn.

2. Using real-time data to gain valuable insights.

3. Using AI to predict churn.

Use the Latest Tools to Implement These Strategies

These methods to make data-driven decisions are, in theory, effective. But they are much easier to implement with the right tools. AI-powered platforms like Keewano offer real-time insights into player engagement, monetization, and retention. 

Powered by an AI-first, real-time database that is 600x faster than any existing data architecture today, Keewano’s agent automates the analysis process effortlessly, empowering your product and analytics teams on the following aspects: 

• Conducting root cause analysis for churn 

• Automatically detecting anomalies

• Automatically identifying product/UX-related issues and suggesting improvements 

• Finding opportunities for monetization.

Bottom line: You want to make data-driven decisions to improve your games. To achieve that, you need next-gen tools to help you understand why players do what they do.

Check out Keewano.com for more valuable insights on how game developers can make impactful, data-driven decisions.

This was a guest post provided by:

Joshua Plotnek

Joshua is Keewano’s Blog Editor-in-Chief, a gaming enthusiast passionate about the connections between games, data, and AI. He covers topics like game development, user behavior, and analytics to bring fresh insights to the blog.